Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Type D and Type F activities
Type D learning activities are the student's choice, type F activities are additional knowledge useful for job placement (internships, transversal skills, project works, etc.). According to the Teaching Regulations of the Course, some activities can be chosen and entered independently in the booklet, others must be approved by a special committee to verify their consistency with the study plan. Type D or F learning activities can be covered by the following activities.
1. Modules taught at the University of Verona
Include the modules listed below and/or in the Course Catalogue (which can also be filtered by language of delivery via Advanced Search).
Booklet entry mode: if the teaching is included among those listed below, the student can enter it independently during the period in which the curriculum is open; otherwise, the student must make a request to the Secretariat, sending the form to carriere.scienze@ateneo.univr.it during the period indicated.
2. CLA certificate or language equivalency
In addition to those required by the curriculum/study plan, the following are recognized for those matriculated from A.Y. 2021/2022:
- English language: 3 CFUs are recognized for each level of proficiency above that required by the course of study (if not already recognized in the previous course of study).
- Other languages and Italian for foreigners: 3 CFUs are recognized for each proficiency level starting from A2 (if not already recognized in the previous study cycle).
These CFUs will be recognized, up to a maximum of 6 CFUs in total, of type F if the study plan allows it, or of type D. Additional elective credits for language knowledge may be recognized only if consistent with the student's educational project and if adequately justified.
Those enrolled until A.Y. 2020/2021 should consult the information found here.
Method of inclusion in the booklet: request the certificate or equivalency from CLA and send it to the Student Secretariat - Careers for the inclusion of the exam in the career, by email: carriere.scienze@ateneo.univr.it
Warning: to students, who have achieved the B2 level of English in their three-year careers (bachelor), we emphasize the need to replace the full B2 level of English, provided by the study plan, with the C1 level of "computerized" English (prova informatizzata) or to acquire other language proficiency in a community language at least at the full B1 level.
3. Transversal skills
Discover the training paths promoted by the University's TALC - Teaching and learning center intended for students regularly enrolled in the academic year of course delivery
Mode of inclusion in the booklet: the teaching is not expected to be included in the curriculum. Only upon obtaining the Open Badge will the booklet CFUs be automatically validated. The registration of CFUs in career is not instantaneous, but there will be some technical time to wait.
4. CONTAMINATION LAB
The Contamination Lab Verona (CLab Verona) is an experiential course with modules on innovation and enterprise culture that offers the opportunity to work in teams with students from all areas to solve challenges set by companies and organisations.
Upon completion of a CLab, students will be entitled to receive 6 CFU (D- or F-type credits).
Find out more: https://www.univr.it/clabverona
PLEASE NOTE: In order to be admitted to any teaching activities, including those of your choice, you must be enrolled in the academic year in which the activities in question are offered. Students who are about to graduate in the December and April sessions are therefore advised NOT to undertake extracurricular activities in the new academic year in which they are not enrolled, as these graduation sessions are valid for students enrolled in the previous academic year. Therefore, students who undertake an activity in an academic year in which they are not enrolled will not be granted CFU credits.
5. Internship/internship period
In addition to the CFUs stipulated in the curriculum/study plan (check carefully what is indicated on the Teaching Regulations): here information on how to activate the internship.
Check in the regulations which activities can be Type D and which can be Type F.
Modules and other activities that can be entered independently in the booklet
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° 2° | Introduction to quantum mechanics for quantum computing | D |
Claudia Daffara
(Coordinator)
|
| 1° 2° | Introduction to Robotics for students of scientific courses. | D |
Andrea Calanca
(Coordinator)
|
| 1° 2° | Mathematics mini courses |
Giacomo Albi
(Coordinator)
|
|
| 1° 2° | Web and mobile app design using react and react native | D |
Graziano Pravadelli
(Coordinator)
|
| 1° 2° | Firmware development with bluetooth low energy (BLE) protocol and freertos operating system | D |
Franco Fummi
(Coordinator)
|
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° 2° | Introduction to Robotics for students of scientific courses. | D |
Andrea Calanca
(Coordinator)
|
| 1° 2° | Mathematics mini courses |
Giacomo Albi
(Coordinator)
|
|
| 1° 2° | Programming Challanges | D |
Romeo Rizzi
(Coordinator)
|
| 1° 2° | Protection of intangible assets (SW and invention)between industrial law and copyright | D |
Mila Dalla Preda
(Coordinator)
|
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° 2° | Cooperative Game Theory in the (Deep) RL Era | D |
Alessandro Farinelli
(Coordinator)
|
Statistical learning (2023/2024)
Teaching code
4S009067
Credits
6
Coordinator
Language
English
Also offered in courses:
- Statistical learning - PART II of the course Master's degree in Mathematics
- Statistical learning - PART I of the course Master's degree in Artificial intelligence
- Statistical learning - PART II of the course Master's degree in Artificial intelligence
Courses Single
Authorized
The teaching is organized as follows:
Learning objectives
The course aims to introduce students to the statistical models used in data science. The foundations of statistical learning (supervised and unsupervised) will be developed by placing the emphasis on the mathematical basis of the different state-of-the-art methodologies. It also aims to provide rigorous derivations of the methods currently used in industrial and scientific applications to allow students to understand their requirements for correct use. Laboratory sessions will illustrate the use of fundamental algorithms and industrial case studies in which the student will be able to learn to analyze real data-sets by means of Python software. At the end of the course the students have to demonstrate the following skills: - knowledge of the main stages of data preparation, model creation and evaluation - ability to develop solutions for feature selection - knowledge and ability to use the main regression and regularization models (e.g., LASSO, Ridge Regression) - knowledge and ability to use the main methods for dimensionality reduction (e.g., Principal Component Regression, Partial Least Squares); - knowledge and ability to use the main methods for classification (e.g., KNN, Logistic Regression, LDA) - knowledge and ability to use the main methods for tree-based regression and classification (e.g., decision tree, random forest) - knowledge and ability to use the main methods for unsupervised data analysis (e.g., K-means clustering, hierarchical clustering)
Prerequisites and basic notions
Python programming basics; basics of statistics. Some basic concepts of programming and statistics will be resumed during the course.
Bibliography
Criteria for the composition of the final grade
The final grade is represented by the arithmetic average of the grades of the two parts (1 and 2) of the course.
